Google Google Cloud Architect Professional Exam Prep
Google Cloud Certified - Professional Cloud Architect (Page 23 )

Updated On: 31-Aug-2026

You have been engaged by your client to lead the migration of their application infrastructure to GCP. One of their current problems is that the on-premises high performance SAN is requiring frequent and expensive upgrades to keep up with the variety of workloads that are identified as follows: 20 TB of log archives retained for legal reasons; 500 GB of VM boot/data volumes and templates; 500 GB of image thumbnails; 200 GB of customer session state data that allows customers to restart sessions even if off-line for several days.
Which of the following best reflects your recommendations for a cost-effective storage allocation?

  1. Local SSD for customer session state data. Lifecycle-managed Cloud Storage for log archives, thumbnails, and VM boot/data volumes.
  2. Memcache backed by Cloud Datastore for the customer session state data. Lifecycle-managed Cloud Storage for log archives, thumbnails, and VM boot/data volumes.
  3. Memcache backed by Cloud SQL for customer session state data. Assorted local SSD-backed instances for VM boot/data volumes. Cloud Storage for log archives and thumbnails.
  4. Memcache backed by Persistent Disk SSD storage for customer session state data. Assorted local SSD-backed instances for VM boot/data volumes. Cloud Storage for log archives and thumbnails.

Answer(s): B

Explanation:

The correct answer is B because it offers the most cost-effective and suitable storage solutions for each workload type described. Let's break down each component:
Customer Session State Data (200 GB): Using Memcache backed by Cloud Datastore is ideal. Memcache provides extremely fast, in-memory caching, perfect for frequently accessed session data, improving application responsiveness. Cloud Datastore provides the persistent, scalable, NoSQL store to back the cache and avoid data loss. This combination offers low latency access while ensuring data persistence. Storing session data in something like persistent disk is overkill because it requires an attached compute instance to work.
Log Archives (20 TB): Lifecycle-managed Cloud Storage is the perfect fit. Log archives are typically accessed infrequently but need to be retained long-term. Cloud Storage, particularly with lifecycle rules to transition data to lower-cost tiers (like Nearline or Coldline) as it ages, provides very cost-effective, durable, and scalable storage. This approach minimizes storage expenses while meeting regulatory requirements.
VM Boot/Data Volumes (500 GB) & Image Thumbnails (500 GB): These are also ideal for Lifecycle-managed Cloud Storage.
While VMs do boot off persistent disks, the images and templates can be stored in cloud storage and are retrieved during VM deployment. Cloud storage is cheaper and suitable for this storage pattern where high-speed access is not needed all the time. Thumbnails are also static files that are served through HTTP so cloud storage is the ideal choice.
Options A, C, and D are less optimal. Option A, using local SSD for the customer session data is more expensive than using a caching solution and is not persistent. Options C and D's reliance on local SSDs for boot volumes adds complexity. Option D's usage of Persistent Disk SSD storage with Memcache is also an overkill. Cloud SQL is not designed for caching, making option C ineffective for session data. Therefore, option B offers a balanced, cost-effective, and scalable solution by leveraging appropriate Google Cloud services for the different workload types, matching storage cost to data access requirements.
Authoritative Links:
Cloud Storage: https://cloud.google.com/storage Cloud Datastore: https://cloud.google.com/datastore Memcached: https://memcached.org/ Storage Classes (Cloud Storage): https://cloud.google.com/storage/docs/storage-classes Cloud Storage Lifecycle Management: https://cloud.google.com/storage/docs/lifecycle



Your web application uses Google Kubernetes Engine to manage several workloads. One workload requires a consistent set of hostnames even after pod scaling and relaunches.
Which feature of Kubernetes should you use to accomplish this?

  1. StatefulSets
  2. Role-based access control
  3. Container environment variables
  4. Persistent Volumes

Answer(s): A

Explanation:

StatefulSets are the correct choice because they are specifically designed to manage the deployment and scaling of stateful applications in Kubernetes. Unlike Deployments, which treat pods as interchangeable, StatefulSets provide unique, stable network identities and persistent storage for each pod. This is crucial for applications that require consistent hostnames, as StatefulSets guarantee that each pod will have a predictable and persistent identity, even after scaling or restarts. Each pod within a StatefulSet receives an ordinal index (e.g., pod-0, pod-1, etc.), which determines its hostname and its persistent volume. This ensures that when a pod is rescheduled, it retains the same hostname. Role-based access control (RBAC) manages permissions within the cluster but doesn't affect hostname persistence. Container environment variables allow configuration but don't manage hostnames. Persistent Volumes provide storage but don't inherently ensure hostnames stability. Therefore, StatefulSets uniquely address the requirement for consistent hostnames among Kubernetes pods, making them the ideal solution.
Here are some authoritative links for further research:
Kubernetes StatefulSets: https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/ Kubernetes Deployments vs StatefulSets: https://kubernetes.io/docs/tutorials/stateful-application/



You are using Cloud CDN to deliver static HTTP(S) website content hosted on a Compute Engine instance group. You want to improve the cache hit ratio.
What should you do?

  1. Customize the cache keys to omit the protocol from the key.
  2. Shorten the expiration time of the cached objects.
  3. Make sure the HTTP(S) header Cache-Region points to the closest region of your users.
  4. Replicate the static content in a Cloud Storage bucket. Point CloudCDN toward a load balancer on that bucket.

Answer(s): A

Explanation:

The optimal choice to improve Cloud CDN's cache hit ratio when serving static website content from a Compute Engine instance group is to customize cache keys, specifically by omitting the protocol (HTTP or HTTPS) from the key (Option A). This is because Cloud CDN, by default, includes the protocol in the cache key. This can lead to cache misses even if the content is identical, but accessed via different protocols (e.g., http://example.com/image.jpg vs. https://example.com/image.jpg ). By removing the protocol from the key, Cloud
CDN recognizes both requests as for the same cached resource, thus increasing the cache hit ratio and reducing the need to fetch the content from the origin server repeatedly. Shortening the expiration time (Option B) would have the opposite effect, forcing more frequent cache invalidations and origin fetches. Cache-Region is not a standard HTTP header (Option C) and does not directly impact caching behavior within Cloud CDN. Replicating content to a Cloud Storage bucket (Option D) is a valid strategy but primarily optimizes for edge serving via Cloud Storage, not for optimizing the cache hit ratio of content originating from Compute Engine instances. Using customized cache keys is a common technique to increase caching efficiency when the differences between requests do not affect the content served.For further research, refer to:
Google Cloud CDN documentation on Cache Keys: https://cloud.google.com/cdn/docs/cache-keys Google Cloud documentation on caching overview: https://cloud.google.com/cdn/docs/caching



Your architecture calls for the centralized collection of all admin activity and VM system logs within your project. How should you collect these logs from both VMs and services?

  1. All admin and VM system logs are automatically collected by Stackdriver.
  2. Stackdriver automatically collects admin activity logs for most services. The Stackdriver Logging agent must be installed on each instance to collect system logs.
  3. Launch a custom syslogd compute instance and configure your GCP project and VMs to forward all logs to it.
  4. Install the Stackdriver Logging agent on a single compute instance and let it collect all audit and access logs for your environment.

Answer(s): B

Explanation:

Option B is the correct answer because it accurately reflects how logging is handled in Google Cloud Platform (GCP) using Stackdriver (now Cloud Logging). Google Cloud automatically captures admin activity logs for most services through Cloud Audit Logs. These logs record actions taken by users and services, providing an audit trail for compliance and security purposes. However, virtual machine (VM) system logs, such as syslog and other application logs, aren't automatically ingested. They require the installation of the Cloud Logging agent on each VM instance. The agent acts as a bridge, forwarding these logs to Cloud Logging for centralized storage and analysis. This combination of automatic admin log collection and agent-based VM log collection provides a comprehensive logging solution. Option A is incorrect as it oversimplifies the process; while Cloud Logging collects admin logs, it does not automatically get VM system logs. Option C presents a less scalable and more complex solution involving a custom syslog server, which is less preferred than using the managed Cloud Logging agent. Option D is incorrect because a single instance can't collect logs from all VMs; an agent needs to be on each VM.
Authoritative Links:
Cloud Logging Overview: https://cloud.google.com/logging/docs/overview Cloud Audit Logs: https://cloud.google.com/logging/docs/audit/ Cloud Logging Agent: https://cloud.google.com/logging/docs/agent/



You have an App Engine application that needs to be updated. You want to test the update with production traffic before replacing the current application version.
What should you do?

  1. Deploy the update using the Instance Group Updater to create a partial rollout, which allows for canary testing.
  2. Deploy the update as a new version in the App Engine application, and split traffic between the new and current versions.
  3. Deploy the update in a new VPC, and use Google's global HTTP load balancing to split traffic between the update and current applications.
  4. Deploy the update as a new App Engine application, and use Google's global HTTP load balancing to split traffic between the new and current applications.

Answer(s): B

Explanation:

The correct answer is B. Deploy the update as a new version in the App Engine application, and split traffic between the new and current versions.
App Engine is designed to manage application versions and traffic splitting seamlessly. Deploying the update as a new version allows you to run it alongside the existing version without disruption. App Engine provides built-in functionality to control the percentage of traffic routed to each version, enabling canary testing. You can gradually increase traffic to the new version while monitoring its performance, ensuring a smooth and safe rollout. This minimizes risk and provides the opportunity to identify any issues before fully committing to the update. Options A, C, and D are less suited for this scenario. Instance Group Updater (A) is primarily for Compute Engine, not App Engine. Setting up a new VPC (C) is an unnecessary level of complexity for an App Engine update, and using a separate App Engine application (D) is also more complex than necessary and defeats the purpose of versioning within a single App Engine service.
Here are some authoritative links for further research:
App Engine Versioning and Traffic Splitting: https://cloud.google.com/appengine/docs/standard/python/how-requests-are-routed Deploying and Managing App Engine Applications: https://cloud.google.com/appengine/docs/standard/python/deploying-and-managing-versions Canary Deployments: https://martinfowler.com/bliki/CanaryRelease.html



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